Point-Based Surface Representation for Shape Similarity and Object Recognition

用于形状相似性和对象识别的基于点的表面表示

• 批准号: 9711853
• 负责人: Martial Hebert
• 金额: $ 25.7万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9711853&HistoricalAwards=false
• 关键词: Point; Based; Surface; Representation; Shape

A critical issue in recognizing 3D objects and in registering surfaces is the definition of a ound measure of similarity between 3D shapes. Although shape similarity metrics have been proposed in the case of 2D shapes, only partial results have been obtained for 3D shapes. Part of the problem is that it is difficult to define the necessary pose-independent reference frame on a 3D surface. This research investigates representations of 3D shapes that lead to a natural definition of shape similarity that can be built directly from point sets without requiring feature extraction, segmentation, or surface fitting. These representations may be adjusted to be either a global representation of a surface (e.g., for object recognition) or a more local representation (e.g., for tracking of local surface patches.) They lead naturally to 3D shape matching algorithms directly applicable to object recognition problems. Beyond their application to 3D object recognition, these representations will provide a framework for the analysis of 3D shapes. Of particular interest is the study of shape classes and generic object recognition. The shape representations and the associated matching algorithms are validated through scenarios motivated by real applications. In one application, object recognition is used for building and identifying geometric models of industrial parts. In a second application, the shape similarity technique is used for registering views of terrain and landmark surfaces in a natural environment for mobile robot navigation and mapping.

在识别3D物体和配准表面中的一个关键问题是定义3D形状之间的相似性的合理度量。虽然形状相似性度量已被提出的情况下，2D形状，只有部分结果已获得3D形状。部分问题在于很难定义必要的姿态独立参考系 对 一 3D 面 这 研究 研究3D形状的表示，导致形状相似性的自然定义，可以直接从点集构建，而不需要特征提取，分割或表面拟合。 这些表示可以被调整为表面的全局表示（例如，用于对象识别）或更局部的表示（例如，用于跟踪局部曲面片。） 它们自然导致直接适用于对象识别问题的3D形状匹配算法。 超出 它们在3D对象识别中的应用， 这些表示将为3D形状的分析提供框架。特别感兴趣的是形状类和通用对象识别的研究。 通过真实的应用场景验证了形状表示和相关的匹配算法。 在一个应用中，对象识别被用于构建和识别对象的几何模型。 工业零件。 在第二应用中， 形状相似性技术用于配准自然环境中的地形和地标表面的视图，以用于移动的机器人导航和地图绘制。